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131 lines
3.6 KiB
131 lines
3.6 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "Layer.h"
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#include "paddle/math/Matrix.h"
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#include "paddle/utils/Logging.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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/**
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* A layer for linear interpolation with two inputs,
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* which is used in NEURAL TURING MACHINE.
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* \f[
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* y.row[i] = w[i] * x_1.row[i] + (1 - w[i]) * x_2.row[i]
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* \f]
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* where \f$x_1\f$ and \f$x_2\f$ are two (batchSize x dataDim) inputs,
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* \f$w\f$ is (batchSize x 1) weight vector,
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* and \f$y\f$ is (batchSize x dataDim) output.
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*
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* The config file api is interpolation_layer.
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*/
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class InterpolationLayer : public Layer {
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protected:
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/// weightLast = 1 - weight
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MatrixPtr weightLast_;
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MatrixPtr tmpMatrix;
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public:
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explicit InterpolationLayer(const LayerConfig& config) : Layer(config) {}
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~InterpolationLayer() {}
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bool init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) override;
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void forward(PassType passType) override;
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void backward(const UpdateCallback& callback = nullptr) override;
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};
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REGISTER_LAYER(interpolation, InterpolationLayer);
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bool InterpolationLayer::init(const LayerMap& layerMap,
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const ParameterMap& parameterMap) {
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/* Initialize the basic parent class */
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Layer::init(layerMap, parameterMap);
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CHECK_EQ(3U, inputLayers_.size());
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return true;
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}
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void InterpolationLayer::forward(PassType passType) {
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Layer::forward(passType);
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MatrixPtr weightV = getInputValue(0);
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MatrixPtr inV1 = getInputValue(1);
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MatrixPtr inV2 = getInputValue(2);
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size_t batchSize = inV1->getHeight();
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size_t dataDim = inV1->getWidth();
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CHECK_EQ(dataDim, getSize());
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CHECK_EQ(dataDim, inV2->getWidth());
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CHECK_EQ(batchSize, inV1->getHeight());
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CHECK_EQ(batchSize, inV2->getHeight());
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{
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REGISTER_TIMER_INFO("FwResetTimer", getName().c_str());
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resetOutput(batchSize, dataDim);
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}
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MatrixPtr outV = getOutputValue();
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Matrix::resizeOrCreate(weightLast_, batchSize, 1, false, useGpu_);
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weightLast_->one();
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weightLast_->sub(*weightV);
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REGISTER_TIMER_INFO("FwInterpTimer", getName().c_str());
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// outV = inV1 * weight + inV2 * weightLast
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outV->addRowScale(0, *inV1, *weightV);
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outV->addRowScale(0, *inV2, *weightLast_);
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}
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void InterpolationLayer::backward(const UpdateCallback& callback) {
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MatrixPtr outG = getOutputGrad();
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MatrixPtr weightV = getInputValue(0);
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MatrixPtr inV1 = getInputValue(1);
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MatrixPtr inV2 = getInputValue(2);
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MatrixPtr inG0 = getInputGrad(0);
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MatrixPtr inG1 = getInputGrad(1);
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MatrixPtr inG2 = getInputGrad(2);
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size_t batchSize = inV1->getHeight();
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size_t dataDim = inV1->getWidth();
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REGISTER_TIMER_INFO("BwInterpTimer", getName().c_str());
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if (inG0) {
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Matrix::resizeOrCreate(tmpMatrix, batchSize, dataDim, false, useGpu_);
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// inG0 += outG .* (inV1 - inV2)
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tmpMatrix->sub(*inV1, *inV2);
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inG0->rowDotMul(0, *outG, *tmpMatrix);
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}
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if (inG1) {
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// inG1 += outG * weight
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inG1->addRowScale(0, *outG, *weightV);
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}
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if (inG2) {
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// inG2 += outG * weightLast
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inG2->addRowScale(0, *outG, *weightLast_);
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}
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}
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} // namespace paddle
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